A Statistical Perspective on Retrieval-Based Models
Authors: Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments |
| Researcher Affiliation | Industry | 1Google, Mountain View, USA 2Google Research, New York, USA 3Google Deep Mind, New York, USA. |
| Pseudocode | No | The paper describes its methods through prose and mathematical formulations, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to code repositories for the described methodology. |
| Open Datasets | Yes | CIFAR-10", "Image Net dataset", "ALIGN (Jia et al., 2021) |
| Dataset Splits | Yes | We randomly generate a train set of size n = 10000 in a 10-dimensional space... and perform a 10-fold cross-validation.", "We randomly partition the data into a train set of size n = 10000 points and remaining 2000 points for test. We do a 10-fold cross-validation.", "We use the standard train-test split with n = 1281167 training and 50000 test examples. |
| Hardware Specification | No | The paper mentions computational cost and model sizes but does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions models and optimizers (e.g., 'Adam optimizer', 'Mobile Net V3') but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For solving the local ERM, we fine-tune a Mobile Net V3 model, which has been pretrained on Image Net, on the retrieved set using Adam optimizer with a linear decay schedule. |